Using Fuzzy Multi-Criteria Decision-Making as a Human-Centered AI Approach to Adopting New Technologies in Maritime Education in Greece

Abstract

The need to review maritime education has been highlighted in the relevant literature. Maritime curricula should incorporate recent technological advances, as well as address the needs of the maritime sector. In this paper, the Fuzzy Delphi Method (FDM) and the Fuzzy Analytic Hierarchy Process (FAHP) are utilized in order to propose a fuzzy multicriteria decision-making (MCDM) methodology that can be used to assess the importance of new technologies in maritime education and design a fuzzy evaluation model that can assist in maritime education policy-making. This study integrates the perspectives of the main maritime education stakeholders, namely, lecturers and maritime sector management. We selected data from a group of 19 experienced maritime professors and maritime business managers. The results indicate that new technologies such as artificial intelligence (AI), augmented and virtual reality (AR/VR), the Internet of Things (IoT), digital twins (DTs), and cybersecurity, as well as eLearning platforms, constitute a set of requirements that maritime education policies should meet by designing their curricula appropriately. This study suggests that fuzzy logic MCDM methods can be used as a human-centered AI approach for developing explainable education policy-making models that integrate stakeholder requirements and capture the subjectivity that is often inherited in their perspectives.

1. Introduction

1.1. Maritime Education—Emerging Issues

Merchant shipping is significant for global trade since it accounts for 80% of international commerce [1]. It is a major business sector not only in Greece but also in the European Union. Greek merchant shipping accounts for 59% of the European Union (EU)-controlled fleet. The Greek shipowners with 5514 ships currently control approximately 21% of the global fleet in terms of capacity [2].

The development of new technologies and advances in artificial intelligence (AI) and augmented/virtual reality (AR/VR), among other technological advances, as well as the trends toward the digitization of shipping, and environmental concerns that have been raised over recent years, change the requirements for the maritime profession and hence maritime education. The need to review the current curricula in maritime education due to technological changes has already been highlighted [3]. The new generation of seafarers should be equipped with the necessary knowledge and skills required by Industry 4.0 and all technologies implied by terms such as (AI), (AR/VR), big data, blockchain, etc. Nardo et al. (2020) [3] proposed a roadmap for universities and merchant naval academies to introduce Industry 4.0 into their curricula. A survey carried out by (Ozdemir et al., 2023) [4] identified three areas that maritime education should place emphasis on. These areas are environmental concerns, the latest technological developments, and innovative changes. The use of technology to address environmental and emission issues was found to be the top concern regarding environmental protection. Information systems, smart technologies, maritime traffic control systems, 3D printing, and autonomous vessel operations were among the top priority topics that should be incorporated into revised maritime curricula.

In this paper, we utilize fuzzy logic in order to assess the importance of new technologies in maritime education and suggest which ones should be incorporated into revised curricula. Fuzzy logic has been applied in many domains and many hybrid versions have shown promising achievements [5]. Although fuzzy logic has been previously used in education [6,7], its use is limited; thus, it is argued [7] that it represents a promising area of research and applications. In maritime education, the FAHP method is used in [8] in order to identify the most optimal maritime education and training methods among face-to-face and online methods. The need for maritime education to adapt to technological and environmental changes is discussed in [8], especially after the increased social demand for online teaching due to the COVID-19 pandemic. The study authors concluded that online methods were particularly important and that the “XR (extended reality) within the metaverse” teaching method had the highest priority. The novelty of the current research lies in its examination of the potential of emerging technologies, such as AI, AR/VR, etc., in maritime education, a topic that has yet to be explored. This study also argues that the use of fuzzy logic provides the means to apply AI in education from a human perspective by considering human conditions and contexts. Human-centered AI can be investigated from two angles: One assumes that AI is under human control [9], which postulates that either humans or AI have total control, and the other angle assumes that AI is based on the human condition [10], which implies that AI algorithms are developed with humanity as the main consideration involving explainable and interpretable judgments that take into consideration the human context and societal phenomena. Fuzzy logic is widely recognized as a human-centered AI approach because it aligns with key principles of human-centered AI, including interpretability, explainability, adaptability, incorporating human reasoning, and handling human uncertainty.

⮚

Fuzzy logic mimics human reasoning since, unlike traditional binary logic, which relies on strict true/false values (0 or 1), fuzzy logic accommodates varying degrees of truth (e.g., 0.3 and 0.7), mirroring the way humans perceive and interpret information.

⮚

Enhancing AI explainability and transparency and the “black box” problem are major challenges in AI, where models make decisions without providing clear explanations. Fuzzy logic enhances interpretability by employing rules that engage linguistic variables that are easily understandable to humans.

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Incorporating human expertise in decision-making since fuzzy logic allows AI to incorporate expert knowledge through rule-based systems, enabling professionals from various fields to actively contribute to the development of AI models.

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Fuzzy logic enhances handling uncertainty in complex decision-making. Human decision-making often involves uncertainty, subjectivity, and incomplete information. Fuzzy logic is specifically designed to model such real-world complexity.

It is argued that among other domains, AI applications in education need to adopt a human-centered approach in order to avoid bias, lack of governance over the problem domain solutions, and violation of human rights [11].

Fuzzy logic addresses the inherent biases and subjectivity in how individuals from diverse cultural backgrounds, age groups, and personal preferences articulate their experiences [12]. By embracing this variability, this paper proposes that fuzzy logic can serve as a valuable tool for developing human-centered AI applications that are more attuned to human nuances, fostering systems that are adaptive, inclusive, and deeply aligned with real-world human behaviors and perspectives.

The remainder of this paper is organized as follows: Section 1. Introduction; Section 2. Materials and Methods; Section 3. Results; Section 4. Discussion; Section 5. Conclusions.

1.2. New Technologies in Maritime Education

Following a comprehensive literature review, this paper discusses the major technological advances that are expected to impact the maritime industry and its education requirements.

Three-dimensional (3D) printing represents a promising technology with many applications in maritime education. In recent years, there has been a growing variety of applications designed to enhance the teaching of concepts and skills related to 3D printing [13]. Studies suggest that 3D printing enhances the understanding of both theoretical and practical concepts by enabling the creation of physical models and the visualization of processes within the classroom [13]. Of note, 3D printing is a widely used technique due to the low cost of the printer and raw materials when compared to other techniques. Applications include combining AR with 3D printing in order to help students understand how to operate a 3D printer, which is essential for their future careers in engineering and technology [14], and using 3D printing to educate engineering students and assist them create objects they could encounter and interact within their daily professional lives [15].

Maritime autonomous surface ships (MASSs) are expected to revolutionize the maritime industry by providing opportunities for greater efficiency, improved safety, cost savings, and reduced environmental impact [16]. MASSs interact with seafarers at different levels of autonomy; thus, it is suggested that the adoption of a human-centered approach to examining the potential of MASSs is significant [17]. Maritime education needs to prepare a new generation of seafarers to interact with MASSs and cope with technological as well as situational challenges when at sea.

AR/VR applications enable students to train in a controlled and safe environment, especially when exploring hazardous scenarios. AR/VR allows for the design and investigation of tailored simulation environments to develop personalized training and carry out longitudinal studies to evaluate the long-term effects of VR training on maritime safety and operational efficiency [18]. However, a major challenge in training design is the variation in training session durations, which makes it difficult to develop standardized training programs [19]. Simulator-based maritime training is a popular approach because it offers a controlled environment, adjustable task difficulty, cost efficiency, and a safe space for practice without risks. Additionally, with the development of AR/VR technologies, virtual maritime simulators are valuable for designing exercises that enable the evaluation and comparison of student performance and learning outcomes [20].

With the rapid development of digital technologies and the expansion of Internet applications, including the IoT, communications, etc., maritime organizations are increasingly focused on cybersecurity breaches that could affect the timeliness of their shipments, thus making training on cyber risk assessment and prevention crucial [21]. As a strong indication of its importance, President Biden issued an Executive Order in 2024 to amend the Espionage Act of 15 June 1917 and Executive Order 10,173. This order aims to enhance the security of key elements of the nation’s maritime transportation system, including ports, terminals, vessels, waterways, and land-based connections [22].

The proliferation of AI and machine learning (ML) applications has caused a profound transformation in the maritime sector. The availability of big data provides the necessary foundation for digital twin development and AI systems to improve or even revolutionize activities such as vessel tracking, weather pattern recognition, and port activity forecasting [23]. AI and digital twins can assist in developing MASSs by implementing algorithms, such as ontology-based knowledge graphs [16], that are expected to improve efficiency and safety onboard as well as reduce the number of people on the ship, thus reducing the number of incidents [24]. Preventive maintenance is an important area of AI in the maritime sector, with deep learning (DL) representing a very popular approach to Prognostics and Health Management (PHM), focusing on condition monitoring, fault diagnosis, and maintenance decision-making [25]. Such advances in AI and ML create new requirements for maritime education. Students should delve into AI and big data analytics and learn how to predict equipment failures and optimize maintenance schedules. By collecting and analyzing data from ships’ critical systems, students should develop skills and capabilities in order to be able to identify patterns of preemptive maintenance, thus reducing the risk of unexpected breakdowns. AI applications are also developed to assist in ship route optimization in order to not only reduce costs but to prevent environmental pollution [1]. Environmental monitoring is improved by AI applications that analyze vast quantities of data pertaining to air, noise, and water pollution. Therefore, training students in using AI-driven tools will help them learn how to reduce fuel consumption and minimize emissions for planning environmentally friendly and cost-effective routes. Traffic management is another area where AI and ML can be successfully applied. In maritime traffic, many ship trajectories within the same traffic environment exhibit similar characteristics such as route, speed, and course. Therefore, AI can identify patterns, thus predicting uncertainties in ship route destinations and optimizing ship temporary stops in inland waterways [26]. Thus, students receiving training in AI can simulate real-time traffic management in busy ports and shipping lanes and develop the necessary skills for simulating real-time traffic management in busy ports. Also, data-driven approaches that apply AI and ML in crew recruitment can address issues pertaining to gender discrimination and occupational, psychological, and personal characteristics, increase the transparency and reliability of recruitment processes, and enable the selection of candidates better suited to the demanding jobs in merchant shipping [27]. Therefore, maritime students can learn how to utilize AI to improve crew recruitment and training, enhance safety, and ensure optimal performance in high-pressure situations.

As the demand for Internet of Things (IoT), Cloud, and Internet on Ship (IoS) applications increases, the deployments of 5G networks open up new opportunities for the maritime industry and hence the education of maritime staff. IoS systems and key port users, such as cranes, trucks, and vehicles, are equipped with sensors, IoT systems, and communication technologies that enable them to collect, process, and share information about their surroundings. This enhances the decision-making process in their daily operations, but it requires 5G networks that can offer speed and quality in maritime communications [28]. The deployment of the necessary infrastructure provides the means and the development of the Maritime Internet of Things (MIoT) is a rapidly expanding communication ecosystem with the potential to revolutionize the shipping industry. The aim of MIoT is to improve the efficiency, safety, and sustainability of maritime operations by leveraging sensors and other interconnected devices effectively [29].

Blockchain applications include supply chain management, logistics, smart contracts, cybersecurity, and the Internet of Things (IoT). Integrating blockchain technology into maritime shipping industry processes spawns new opportunities that would benefit the business, particularly in cases such as ensuring secure document traceability and verifying the origin of goods for all stakeholders [30].

Intensified competition, increased customer expectations, the pressure to reduce costs, and the need for compliance with standards have led maritime businesses to consider digitalization. Digital servitization helps companies stay competitive and enhance value capture by offering greater value than traditional tangible products and standalone add-on services [31]. The maritime industry has increasingly adopted technologies such as remote monitoring, cloud computing, cybersecurity, big data, real-time connectivity, and advanced platforms [32]. Therefore, the growing trend of digitalization is a key driver of digital servitization in the maritime sector, thus raising the educational needs of seafarers.

Table 1. Maritime technologies used in the maritime sector.

n	Technology Types
1	3D printing
2	Autonomous surface ships
3	Augmented/virtual reality
4	Cybersecurity and safety
5	Artificial intelligence/digital twins/big data analytics
6	Communication technologies and 5G
8	Internet of Things (IoT)
9	Simulation/simulators
10	Digital servitization
11	Blockchain

shows the technologies that are expected to play an important role in maritime and that should thus be included in maritime education curricula.

Emerging technologies in maritime education are expected to not only enhance the quality of training but also foster inclusivity and accessibility. As a result, students from less privileged backgrounds will have greater access to training opportunities, geographic limitations will be reduced, and personalized learning will be facilitated, creating a more inclusive educational environment.

AI in maritime education offers a transformative approach to ensuring equitable learning opportunities for all students, regardless of their abilities, backgrounds, or geographic location. By leveraging AI technologies, maritime education can be personalized, accessible, and more adaptable to diverse student needs, providing the tools and resources necessary to overcome traditional barriers in maritime training [33].

AI-powered simulations and virtual reality (VR) systems play a crucial role in delivering inclusive, hands-on learning experiences that are accessible to students, regardless of geographic constraints. Maritime education typically requires practical training, such as ship operation, navigation, and emergency procedures, which can be expensive and challenging to access through traditional methods. AR and VR technologies facilitate remote training and collaboration, enabling maritime students and professionals to engage in training sessions and simulations from anywhere in the world. This is especially advantageous for seafarers who spend extended periods at sea, as it allows them to continue their training and skill development without needing to be physically present at a training facility [34]. AI can also assist in bridging language barriers and make educational content more inclusive for non-native speakers by providing real-time translations and speech-to-text capabilities, enhancing communication in international maritime settings. AI-based chatbots and virtual assistants can provide on-demand support to students, helping them navigate through the course material, answer frequently asked questions, and even provide emotional support. This ensures that all students, including those in remote areas, can access help at any time [35].

AR/VR as well as simulation technologies can facilitate inclusive education. Maritime education traditionally involves significant amounts of practical, hands-on training that requires access to real ships, training simulators, or field-based activities. However, these can be cost-prohibitive and physically inaccessible for some students, particularly those with mobility impairments or those from remote areas [34].

Maritime training often requires expensive equipment and physical ship components that many institutions cannot afford. 3D printing reduces costs by allowing institutions to create functional ship components, engine parts, and training tools at a fraction of the price. Furthermore, incorporating 3D printing technology into the education system offers valuable opportunities for different learning styles, especially hands-on learning. It enables students to experience the full process, from conceptualizing their ideas to physically creating their projects [36].

2. Materials and Methods

2.1. Materials and Methodology

This study proposes a fuzzy multi-criteria decision-making methodology that can be used to integrate maritime education stakeholders’ views, assess the importance of new technologies with respect to education quality, and assist in maritime education policy-making. Thus, the aims of this study are as follows:

• Identifying the important technologies that should be taken into consideration in maritime education policy-making.
• Assessing the relative importance of new technologies in maritime education.
• Developing a fuzzy technology evaluation model that would assist in designing the portfolio of new technologies required to improve maritime education quality and assist in educational policy-making.

A group of 19 experts agreed to participate in the study. The group consisted of 10 maritime professors teaching various subjects and 9 managers from the maritime industry in Greece. To tackle the problem of data scarcity and ambiguity found in many real-world scenarios, fuzzy logic methods have been developed and widely adopted in conjunction with MCDM methods [37]. By utilizing fuzzy logic methods, research studies can be conducted and empirically assessed even with small sample sizes [38]. It is argued that the number of experts in a panel should be between 10 and 50 [39]. In this study, we performed a thorough review of the relevant literature in order to identify current as well as future technologies and their applications in maritime education. Next, a questionnaire was designed and sent to the 19 experts in order to capture their views regarding the importance of the identified technologies and their corresponding use cases in maritime education. There were three types of questions in the questionnaire:

(1)

What are the most important Information and Communication Technologies (ICT) that according to your view will impact maritime education?

(2)

What is their expected impact?

(3)

What are the possible use cases of each technology in maritime education?

Figure 1 illustrates the steps of the research methodology adopted in this study. Following a comprehensive literature review to identify emerging technologies in the maritime sector, an FDM questionnaire was developed. Experts participating in this study were then asked to evaluate the significance of each technology. Their responses were gathered through semi-structured interviews using the FDM questionnaire. The collected data were analyzed using the FDM to shortlist technologies for which consensus had been reached regarding their importance.

Next, the FAHP was applied to determine the relative importance of these emerging technologies in relation to maritime education quality. To facilitate this, a second questionnaire was designed, allowing experts to perform pairwise comparisons and provide insights into the significance of each technology and its corresponding use cases. The FAHP model was then used to assess the relative importance of the identified technologies for maritime education. Data analysis was conducted using MS Excel.

MCDM has long been a reliable tool for making precise decisions. The hybrid MCDM approach, which combines the Delphi method with FAHP analysis, integrates views and insights from experts and stakeholders, ensuring a thorough assessment of emerging technologies’ importance in maritime education. By incorporating fuzzy sets, this model effectively manages uncertainties and ambiguities in decision-making, allowing for qualitative evaluation despite imprecisions in expert judgments and preferences. Furthermore, the Delphi method facilitates consensus-building through iterative feedback, strengthening the credibility of the final decisions. By integrating multiple MCDM methods, this hybrid approach harnesses the strengths of each method, leading to more comprehensive and accurate decisions. It minimizes dependence on any single methodology, enhancing the robustness and reliability of the decision-making process. By incorporating these MCDM tools, this study establishes a structured framework for identifying, evaluating, and prioritizing emerging technologies in maritime education, enabling stakeholders to make well-informed decisions regarding their selection, prioritization, and implementation.

2.2. Methods

This study represents experts’ answers both in the FDM and the FAHP as triangular fuzzy numbers (TFNs). TFNs are represented by a triple (a, b, c). The membership function $f_A\left(x\right)$ of TFN $\tilde{A}$ can be derived using the following equation adopted from (Lin et al., 2007) [40].

$$f_A\left(x\right)=\left\{\begin{array}{ccc}{\displaystyle \frac{x-c}{a-c}}& ,& c\le x\le a\\ & & \\ {\displaystyle \frac{b-x}{b-a}}& ,& a\le x\le b\\ & & \\ 0& ,& otherwise\end{array}\right.$$

(1)

where a, b, c are real numbers.

2.2.1. The Fuzzy Delphi Method

The FDM is an extension of the traditional Delphi method introduced by (Dalkey, N. C.; Helmer, 1963) [41]. The FDM has been extensively used in many research studies across a variety of domains. It takes the opinions expressed by experts by adopting a linguistic perspective utilizing fuzzy numbers. Thus, the FDM reduces the ambiguity in experts’ opinions [42]. It is claimed that the FDM is a current trend of expert consultation and objectivity assurance [43]. It has been used in order to seek consensus among experts on MCDM problems such as in constructing an index of safety criteria in occupational health [44], developing a software usability index [39], developing road safety performance indicators [45], web adaptation [46], and optimizing the critical artificial intelligence factors influencing cost management in civil engineering projects [47]. In studies pertaining to educational matters, the FDM has been applied to measuring performance in higher education [48], blending learning [42], and developing indicators for sustainable campus development for Taiwan’s Ministry of Education [49]. The steps of the FDM are as follows:

Step 1: Collect experts’ opinions. The experts provide their answers by using linguistic variables in questionnaires. This study utilizes TFNs to capture expert consensus because they are well-suited for aggregating expert opinions [50] and more closely resemble human thinking [51].

Step 2: Aggregate experts’ opinions. Aggregated opinions are represented by a TFN that is denoted simply as a triple (${tl}_q,{tm}_q,{tu}_q$), where

$${tl}_q=\mathit{min}(e_{p,q})$$

(2)

represents the lowest of all experts’ judgment,

$${tm}_q=\sqrt{{\prod }_{p=1}^z{e}_{p,q}}$$

(3)

is the geometric means of $e_{p,q}$, indicating the aggregation of all experts’ judgments, and

$${tu}_q=\mathit{max}(e_{p,q})$$

(4)

represents the highest of all experts’ judgment, where p = 1,…,z and q = 1,…,k represent the number of experts and the number of technologies, respectively, and the $e_{p,q}$ represents the response of the pth expert regarding the importance of the qth technology with respect to its potential in maritime education.

Thus, the TFN ${tw}_q=({tl}_q,{tm}_q,{tu}_q)$ represents the importance of the pth technology.

Step 3: Defuzzify the technology importance t$w_q$. This study uses the center of gravity method of defuzzification to obtain the defuzzified value $S_q$ according to the following formula [52]:

$$S_q={\displaystyle \frac{{tl}_q+{tm}_q+{tu}_q}{3}}$$

(5)

Step 4: Set threshold (a) to screen out technologies that the experts do not reach consensus to consider as most important. It should be noted that the consensus threshold value (a) has been given different values in previous studies. This study adopts the approach applied in [52]. Thus, there was a consensus among the experts who participated in this study to set the threshold (a) = 7. Thus,

If $S_q\ge 7$, then technology (q) is considered as important.

If $S_q<7$, then technology (q) is excluded from further analysis.

2.2.2. The Fuzzy Analytic Hierarchy Process

The FAHP extends the Analytic Hierarchy Process (AHP) developed by (Saaty, 1986) [53] by utilizing fuzzy logic. The FAHP utilizes fuzzy numbers to overcome verbal ambiguity in judgments and to represent the opinions expressed by experts. FAHP develops a hierarchical structure of criteria, performs pairwise comparisons among the criteria, and aims to prioritize the criteria by calculating their relative weights. It is widely used in studies pertaining to technology product evaluation [54], intellectual capital evaluation and its contribution to university performance [55], customer satisfaction [56], and website selection in programmatic advertising [57].

Assume that $A={(a_{ij})}_{nxm}$ is a fuzzy pairwise comparison judgment matrix and $M=(l,m,u)$ is a TFN. According to the FAHP, each object is taken and extent analysis for each goal (gi) is performed respectively. Therefore, m extent analysis values for each object can be obtained, with the following notation:

$$M_{g_i}^1,M_{g_i}^2,\dots ,M_{g_i}^m,\mathrm{i}=1,2,\dots ,\mathrm{n}$$

(6)

where all of the $M_{g_i}^j(j=1,2,\dots ,m)$ are triangular fuzzy numbers.

The steps used for the FAHP are as follows:

1. The value Si of the fuzzy synthetic extent with respect to the ith object is defined as follows:

$$S_i={\displaystyle \sum _{j=1}^m{M}_{g_i}^j}\otimes {\left[{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}}\right]}^{-1}$$

(7)

$$\mathrm{s}.\mathrm{t}.{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}=\left({\displaystyle \sum _{j=1}^m{l}_j,{\displaystyle \sum _{j=1}^m{m}_j,}{\displaystyle \sum _{j=1}^m{u}_j}}\right)$$

(8)

$${\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}}=\left({\displaystyle \sum _{i=1}^n{l}_i,{\displaystyle \sum _{i=1}^n{m}_i,}{\displaystyle \sum _{i=1}^n{u}_i}}\right)$$

(9)

Next, compute the inverse of the vector in Equation (4) such that

$${\left[{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}}\right]}^{-1}=\left({\displaystyle \frac{1}{{\displaystyle \sum _{i=1}^n{u}_i}}},{\displaystyle \frac{1}{{\displaystyle \sum _{i=1}^n{m}_i}}},{\displaystyle \frac{1}{{\displaystyle \sum _{i=1}^n{l}_i}}}\right)$$

(10)

The TFN value of $S_i=(l_i,m_i,u_i)$ is calculated using Equations (2)–(5).

2. The degree of possibility of $S_j=(l_j,m_j,u_j)\ge S_i=(l_i,m_i,u_i)$ is defined as follows:

$$V(S_j\ge S_i)=\underset{y\ge x}{\sup }\left[\min ({\mu }_{S_i}(x),{\mu }_{S_j}(y))\right]$$

(11)

which can be equivalently expressed as follows:

$$\begin{array}{c}V(S_j\ge S_i)=height(S_i\cap S_j)={\mu }_{S_j}(d)\\ =\left\{\begin{array}{cc} 1,& if m_j\ge m_i\\ 0,& if l_i\ge u_j\\ {\displaystyle \frac{l_i-u_j}{(m_j-u_j)-(m_i-l_i)}},& otherwise\end{array}\right\}\end{array}$$

(12)

where d is the ordinate of the highest intersection point D between ${\mu }_{S_i}$ and ${\mu }_{S_j}$ (Figure 2).

In order to compare the Si and Sj, we need both the values of $V(S_i\ge S_j)$ and $V(S_j\ge S_i)$.

3. The minimum degree of possibility for a convex fuzzy number to be greater than k convex fuzzy numbers Si (i = 1, 2,…, k) can be defined by

$$\begin{array}{l}V(S\ge S_1,S_2,\dots ,S_k)=V\left[\left(S\ge S_1\right) and \left(S\ge S_2\right) and \dots and (S\ge S_k) \right]\\ =\min V(S\ge S_i), i=1,2,3,\dots ,k.\end{array}$$

(13)

Assume that

$$d^{\prime }\left(A_i\right)=\mathit{min}V\left(S_i\ge S_k\right),fork=1,2,\dots ,nandk\ne i$$

(14)

Then the weight vector is given by

$$W^{\prime }={(d^{\prime }(A_1),d^{\prime }(A_2),\dots ,d^{\prime }(A_n))}^T$$

(15)

where Ai (i = 1, 2,…, n) are n elements.

4. Obtain the normalized weight vectors as follows:

$$W={(d(A_1),d(A_2),\dots ,d(A_n))}^T$$

(16)

where W is a non-fuzzy number, and it represents the priority weights of one alternative over another.

5. Calculating the Consistency Ratio (CR)

The CR is calculated by adopting the approach used in (Jakiel, & Fabianowski, 2015) [58], who computed CR for modal values of the fuzzy numbers in the pairwise matrices. Therefore, this paper computes the CR according to the following formulas of the classical AHP method:

$$CI={\displaystyle \frac{{\lambda }_{\max }-n}{n-1}}$$

(17)

and

$$CR={\displaystyle \frac{CI}{RI(n)}}$$

(18)

where

${\lambda }_{\max }$ is the maximum eigenvalue of the pairwise matrix A made out of modal values of fuzzy numbers,

n is the number of rows of matrix A, i.e., the number of criteria used in the FAHP model, and

RI is the random index of inconsistency, whose values are shown in

Table 2. Values of the RI index in relation to n.

N	RI
1	0.00
2	0.00
3	0.58
4	0.90
5	1.12
6	1.24
7	1.32
8	1.41
9	1.45
10	1.49

[59].

A matrix A is consistent if the corresponding CR < 0.1.

Several research studies have combined the FDM and the FAHP in problems pertaining to modeling technology product evaluation [54], technology selection [52], the ranking of the main organizational resilience indicators in a hospital [60], assessing the key barriers to the adoption and use of solar water heaters [61], and assessing the effectiveness of the water supply service in Algeria [62].

3. Results

3.1. Determining Important Technologies

The FDM was utilized to obtain the experts’ opinions and identify the most important technologies in maritime education. The experts were shown the technologies that are expected to play an important role in maritime education, with reference to Table 1. Then, they were asked to report their judgments regarding the importance of these technologies in maritime education. The linguistic scale and the corresponding TFNs that were used by each expert to provide his/her judgments were adopted from [63] and they are shown in

Table 3. Linguistic scale and the corresponding TFNs used in the FDM.

Linguistic Term	Not Important	Somewhat Important	Important	Very Important	Extremely Important
Triangular Fuzzy Number	(0, 1, 3)	(1, 3, 5)	(3, 5, 7)	(5, 7, 9)	(7, 9, 10)

.

Table 4. Experts judgments regarding 3D printing and autonomous surface ships.

Expert	3D Printing $(\mathbf{l},\mathbf{m},\mathbf{u})$			Autonomous Surface Ships (l, m, u)
E1	5	7	9	5	7	9
E2	1	3	5	5	7	9
E3	3	5	7	7	9	10
E4	1	3	5	7	9	10
E5	3	5	7	5	7	9
E6	7	9	10	5	7	9
E7	5	7	9	7	9	10
E8	3	5	7	5	7	9
E9	5	7	9	7	9	10
E10	5	7	9	5	7	9
E11	5	7	9	7	9	10
E12	5	7	9	5	7	9
E13	1	3	5	7	9	10
E14	5	7	9	7	9	10
E15	3	5	7	5	7	9
E16	5	7	9	7	9	10
E17	1	3	5	5	7	9
E18	3	5	7	5	7	9
E19	3	5	7	7	9	10

shows the experts’ judgments on “3D Printing” and “Autonomous Surface Ships” technologies. For example, expert E1 judged both “3D Printing” and “Autonomous Surface Ships” as “Very Important”. Thus, the corresponding TFN was (5, 7, 9) for both technologies.

The application of Formulas (2)–(4) calculates the $W_{3DPrinting}$ and $W_{AutonomousSurfaceShips}$ TFNs that represent the importance of “3D Printing” and “Autonomous Surface Ships” technologies for the maritime education, respectively. Thus,

$$W_{3DPrinting}=(1,5.33,10)\mathrm{and}{W}_{AutonomousSurfaceShips}=(5,7.88,10).$$

The defuzzified values were obtained by applying Formula (5):

$$S_{3DPrinting}=5.44\mathrm{and}{S}_{AutonomousSurfaceShips}=7.62$$

Therefore, “Autonomous Surface Ships” technology was identified as important in maritime education since its defuzzified weight exceeded the threshold. Subsequently, “3D Printing” was excluded from further analysis.

Table 5. The experts selected 5 technologies as the most important in maritime education.

Autonomous Surface Ships	Augmented Virtual Reality	Cybersecurity and Safety	AI/Digital Twins/Big Data	Simulation
7.62	8.01	7.89	8.77	8.11

shows that five technologies exceeded the threshold. Thus, they were selected by the experts as the most important technologies in maritime education.

3.2. Determining Important Technologies’ Use Cases

By using the FDM, the experts who participated in this study were asked to identify the most important use cases for each of the five selected technologies.

Table 6. Use cases of the 5 most important technologies in maritime education.

Autonomous Surface Ships	Augmented Virtual Reality	Cybersecurity and Safety	AI/Digital Twins/Big Data	Simulation
Trainees can learn how to program, monitor, and intervene in the operations of autonomous ships under various conditions.	Ship Navigation Training.	Cybersecurity Threat Simulation and Response Training.	Predictive Maintenance Training.	Bridge and Navigation Training.
Students can use this data for analysis and decision-making exercises and learn how to optimize ship operations.	Engine Room Operations and Maintenance. AR/VR overlays digital information onto physical engine components, guiding trainees through maintenance procedures, troubleshooting, and repairs.	Secure Communication Systems Training.	Voyage Optimization and Route Planning. Students can use AI-driven tools that analyze vast datasets, including weather patterns, ocean currents, and historical voyage data, to optimize ship routes.	Simulations of Engine Room Operation and Crisis Management.
Students can work on improving algorithms, testing new sensor technologies, or developing innovative applications for autonomous systems.	Safety Drills and Emergency Response.	Students train on how to design, implement, and audit Safety Management Systems (SMS) that incorporate cybersecurity protocols.	Real-Time Maritime Traffic Management. Students can learn how to manage vessel movements, avoid collisions, and optimize port operations by analyzing live data feeds.	Port and Vessel Traffic Management. Students practice coordinating the movement of ships in and out of ports, managing docking operations, and ensuring the safe passage of vessels.
	Bridge Team Management. Students practice communication, decision-making, and coordination during complex operations.		Training on Environmental Monitoring and Compliance.
	Real-time guidance to trainees by remote experts.		Crew Performance and Behavioral Analytics. Students can learn how to use these insights to improve crew training, enhance safety, and ensure optimal performance in high-pressure situations.

shows the use case for each technology.

3.3. Calculating the Relative Importance of New Technologies in Maritime Education

The FAHP was used to assess the relative importance of the leading technologies in maritime education identified by the group of experts. The FAHP hierarchy considered is shown in Figure 3. Thus, Figure 3 depicts the technologies considered in this study and the goal of the FAHP model, which is to calculate the relative importance of the new technologies in maritime education.

The experts were asked to make pairwise comparisons regarding the relative importance of the technologies. To report their judgments, TFNs were used since studies argue that TFNs are closer to human thinking [51]. Various linguistic scales have been employed in FAHP studies. This study adopted the TFNs proposed by (Kilincci & Onal, 2011; Lee, et al., 2008) [51,64].

Table 7. Linguistic variables and the corresponding TFNs.

Linguistic Scale	Triangular Fuzzy Number	Triangular Fuzzy Reciprocal Number
Equally important	(1, 1, 1)	(1, 1, 1)
Weakly important	(2/3, 1, 3/2)	(2/3, 1, 3/2)
Fairly more important	(3/2, 2, 5/2)	(2/5, 1/2, 2/3)
Strongly more important	(5/2, 3, 7/2)	(2/7, 1/3, 2/5)
Extremely more important	(7/2, 4, 9/2)	(2/9, 1/4, 2/7)

shows the linguistic variables and corresponding TFNs.

The geometric mean was used to aggregate the experts’ responses as it is considered more effective in reflecting consolidated expert opinions than measures such as the mean or median [45,65]. The pairwise matrix of the aggregated experts’ judgments is shown in

Table 8. The pairwise matrix of the experts’ judgments.

	AuTS	AR/VR	C&S	AI/DT/BD	S
AuTS	(1.000, 1.000, 1.000)	(0.222, 0.545, 1.500)	(0.286, 1.431, 4.500)	(0.286, 2.024, 4.500)	(0.667, 2.595, 4.500)
AR/VR	(0.667, 1.835, 4.500)	(1.000, 1.000, 1.000)	(1.500, 3.067, 4.500)	(0.400, 2.195, 4.500)	(0.667, 2.892, 4.500)
C&S	(0.222, 0.699, 3.500)	(0.222, 0.326, 0.667)	(1.000, 1.000, 1.000)	(0.222, 1.282, 4.500)	(0.400, 2.048, 4.500)
AI/DT/BD	(0.222, 4.494, 3.500)	(0.222, 0.456, 2.500)	(0.222, 0.780, 4.500)	(1.000, 1.000, 1.000)	(0.667, 2.352, 4.500)
S	(0.222, 0.385, 1.500)	(0.222, 0.349, 1.500)	(0.222, 0.488, 2.500)	(0.222, 0.425, 1.500)	(1.000, 1.000, 1.000)

.

The CR = 0.02 < 0.1 was calculated by applying Formulas (17) and (18), thus indicating that the pairwise matrix is consistent. The fuzzy synthetic extent values with respect to the five leading maritime technologies—S-AuTS, S-AR/VR, S-C&S, S-AI/DT/BD, and S-S—were obtained using Formulas (7)–(10). Firstly, by applying Formula (8) for the AuTS, the following calculations were performed:

$$\begin{array}{c}{\displaystyle \sum _{j=1}^m{l}_j}={\displaystyle \sum _{j=1}^5{l}_j}=(1+0.222+0.286+0.286+0.667)=2.460\\ {\displaystyle \sum _{j=1}^m{m}_j}={\displaystyle \sum _{j=1}^5{m}_j}=(1+0.545+1.431+2.024+2.595)=7.594\\ {\displaystyle \sum _{j=1}^m{u}_j}={\displaystyle \sum _{j=1}^5{u}_j}=(1+1.5+4.5+4.5+4.5)=16\end{array}$$

The application of the results of Formula (8) for all five maritime technologies are shown in

Table 9. Application of the results of Formula (8).

${\displaystyle \sum _{\mathit{i}=1}^{\mathit{n}}}$	${\displaystyle \sum _{\mathit{j}=1}^{\mathit{m}}{\mathit{M}}_{{\mathit{g}}_{\mathit{i}}}^{\mathit{j}}}=$	$\left({\displaystyle \sum _{\mathit{j}=1}^{\mathit{m}}{\mathit{l}}_{\mathit{j}}}\right.$	${\displaystyle \sum _{\mathit{j}=1}^{\mathit{m}}{\mathit{m}}_{\mathit{j}}}$	$\left.{\displaystyle \sum _{\mathit{j}=1}^{\mathit{m}}{\mathit{u}}_{\mathit{j}}}\right)$
$i=1$	AuTS	2.460	7.594	16
$i=2$	AR/VR	4.233	10.959	19
$i=3$	C&S	2.067	5.355	14.167
$i=4$	AI/DT/BD	2.333	5.082	16
$i=5$	S	1.889	2.648	8
	${\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}}=$	12.983	31.638	73.167

.

Thus, by applying Formulas (9) and (10), we obtained the following:

$$\begin{array}{l}{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}}=\left({\displaystyle \sum _{i=1}^n{l}_i,{\displaystyle \sum _{i=1}^n{m}_i,}{\displaystyle \sum _{i=1}^n{u}_i}}\right)=(12.983,31.638,73.167)\\ {\left[{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{M}_{g_i}^j}}\right]}^{-1}=\left({\displaystyle \frac{1}{{\displaystyle \sum _{i=1}^n{u}_i}}},{\displaystyle \frac{1}{{\displaystyle \sum _{i=1}^n{m}_i}}},{\displaystyle \frac{1}{{\displaystyle \sum _{i=1}^n{l}_i}}}\right)=\left({\displaystyle \frac{1}{73.167}},{\displaystyle \frac{1}{31.638}},{\displaystyle \frac{1}{12.983}}\right)\end{array}$$

Next, by applying Formula (7), the values of the fuzzy synthetic extent were as follows:

S-AuTS = (2.460, 7.594, 16) ⊗ (1/73.167, 1/31.638, 1/12.983) = (0.034, 0.240, 1.232)
S-AR/VR = (4.233, 10.959, 19) ⊗ (1/73.167, 1/31.638, 1/12.983) = (0.058, 0.346, 1.464)
S-C&S = (2.067, 5.355, 14.167) ⊗ (1/73.167, 1/31.638, 1/12.983) = (0.028, 0.169, 1.091)
S-AI/DT/BD = (2.333, 5.082, 16) ⊗ (1/73.167, 1/31.638, 1/12.983) = (0.032, 0.161, 1.232)
S-S = (1.889, 2.648, 8) ⊗ (1/73.167, 1/31.638, 1/12.983) = (0.026, 0.084, 0.616).

Next, Formulas (12) and (13) returned the degree of possibility of $S_j=(l_j,m_j,u_j)\ge S_i=(l_i,m_i,u_i)$.

Table 10. Application of the results of Formulas (12) and (13).

V(S-AuTS >= Sj)	V(S-AR/VR >= Sj)	V(S-C&S >= Sj)	V(S-AI/DT/BD >= Sj)	V(S-S >= Sj)
V(S-AuTS >= S-AR/VR)	V(S-AR/VR >= S-AuTS)	V(S-C&S >= S-AuTS)	V(S-AI/DT/BD >= S-AuTS)	V(S-S >= S-AuTS)
0.917	1	0.937	0.938	0.788
V(S-AuTS >= S-C&S)	V(S-AR/VR >= S-C&S)	V(S-C&S >= S-AR/VR)	V(S-AI/DT/BD >= S-AR/VR)	V(S-S >= S-AR/VR)
1	1	0.854	0.863	0.680
V(S-AuTS >= S-AI/DT/BD)	V(A-AR/VR >= S-AI/DT/BD)	V(S-C&S >= S-AI/DT/BD)	V(S-AI/DT/BD >= S-C&S)	V(S-S >= S-C&S)
1	1	1	1	0.873
V(S-AuTS >= S-S)	V(S-AR/VR >= S-S)	V(S-C&S >= S-S)	V(S-AI/DT/BD >= S-S)	V(S-S >= S-AI/DT/BD)
1	1	1	1	0.844

shows the results.

By applying Formula (14), we obtained the following:

d’(S-AuTS) = min V (S-AuTS ≥ S-AR/VR, S-C&S, S-AI/DT/BD, S-S) = 0.917
d’(S-AR/VR) = min V (S-AR/VR ≥ S-AuTS, S-C&S, S-AI/DT/BD, S-S) = 1
d’(S-C&S) = min V (S-C&S ≥ S-AuTS, S-AR/VR, S-AI/DT/BD, S-S) = 0.854
d’(S-AI/DT/BD) = min V (S-AI/DT/BD ≥ S-AuTS, S-AR/VR, S-C&S, S-S) = 0.863
d’(S-S) = min V (S-S ≥ S-AuTS, S-AR/VR, S-C&S, S-AI/DT/BD) = 0.680

Finally, by applying Formula (15), we obtained the following weight vector:

W’ = (0.917, 1.000, 0.854, 0.863, 0.680)^T.

The normalized weights were calculated by applying Formula (16). Thus,

W = (0.213, 0.232, 0.198, 0.200, 0.158)^T.

The normalized weights indicate the relative importance of each technology for maritime education. The results indicate the following:

• AR/VR technologies should be prioritized for incorporation into maritime education,
• followed by autonomous ships,
• artificial intelligence, digital twins and big data,
• cybersecurity, and
• simulation, respectively.

Following the calculation of the technologies’ relative priorities, the 19 experts were asked to judge the relative importance of the use cases identified for each technology. Applying the FAHP to each technology and its associated use cases yielded the following priorities: With regard to AR/VR,

• training for ship navigation is identified as the top-priority use case,
• followed by safety drills,
• engine room maintenance,
• bridge team management, and
• remote support from experts.

With respect to autonomous ships,

• the top priority is training students to program, monitor, and intervene in the operations of autonomous ships under various conditions,
• followed by developing skills to analyze data and
• improve algorithms.

Regarding cybersecurity,

• threat simulation training is the most important use case,
• followed by skills required for secure communication and
• knowledge to implement safety management systems (SMSs).

AI, digital twins, and big data provide a portfolio of many training opportunities in maritime education.

• Predictive maintenance is the top priority,
• followed by real-time maritime traffic management,
• voyage optimization,
• environmental monitoring and crew performance, and
• behavioral analytics.

Finally, simulation, although already included in many maritime education curricula, remains crucial for

• bridge and navigation training,
• engine room operations, and
• port and vessel traffic management.

4. Discussion

The technological advances in the maritime industry are creating new opportunities to enhance business efficiency and competitiveness while also being expected to transform the seafarer profession. Consequently, maritime staff, either on land or at sea, need to acquire and develop new skills so that they are able to take advantage of the new technologies for their professional development and to contribute effectively to achieving their organization’s goals. It is well recognized in the relevant literature that maritime education should reconsider its curricula and incorporate the advances in new technologies. Various technologies, such as AI, digital twins, big data, and AR/VR, can be harnessed to drive significant advancements in the maritime sector. For example, AR/VR has been used in two classes of first-year Bachelor of Science Marine Transportation (BS MT) students from the Maritime Academy of Asia and the Pacific (MAAP) [66]. The results showed that students acknowledged the immersive experience during the training sessions, with the majority of students reporting that they would suggest the adoption of VR in their education. The Singapore Maritime Academy (SMA) also reported on the great potential of using (AR), virtual reality (AR), and artificial intelligence (AI) in their Engine Room Simulator training programs [66]. AR/VR has also been used in safety training by the Lloyd’s Register showcased during the last SPE Offshore Europe, the Korean Register, Mitsui O.S.K. Lines, Ltd. (Tokyo, Japan), and the K Line LNG Shipping (UK) Ltd. (London, UK), which uses the Propel 3D simulation tool to train crew onboard [67]. Also, Seably, the brainchild of the Swedish Shipowners’ Association, launched a maritime education platform in 2020, promoting digitization in maritime education by adopting big data, AI, and 3D videos in their training programs, thus allowing for the development of personalized educational content and looking at each learner’s behavior at a granular level [68]. The Maritime Academy of Asia and the Pacific (MAAP) incorporates 3D printing as a support tool in delivering its academic curricula [36]. Including 3D printing in maritime education can allow various learning styles to be practiced, including hands-on learning starting from the project conceptualization to its actual completion.

The management priorities and digital maturity of businesses largely determine the extent to which maritime organizations can harness the benefits of these technologies. Thus, the design of new curricula and education policies should reflect not only technological trends but also the priorities of the maritime businesses.

This study reviews the leading technological trends in the maritime sector and identifies their most promising use cases in maritime education. To achieve these aims, this research analyzes data from a group of 19 experts (maritime professors and professionals) whose judgments were expressed in terms of fuzzy linguistic variables. This study proposes that fuzzy logic can be utilized to develop education policy-making models that capture the unique characteristics of different industries and educational environments. These models would be explainable, supporting the development of human-centered AI approaches, particularly in the crucial field of education. By incorporating the complexities of various contexts, fuzzy logic can enhance the adaptability and transparency of educational policies, ensuring that they align with both industry needs and educational goals.

This study highlights five technologies and several corresponding use cases whose catalytic impact on the maritime industry and education is widely recognized by the experts involved in this research. The technologies and their corresponding use cases, listed in order of relative importance, are as follows:

Firstly, AR/VR can generate highly realistic simulations of a wide range of navigational scenarios, including navigating through thick fog, conducting operations at night, or managing traffic in congested waterways. Students can experience immersive AR/VR environment cases in order to practice how to handle diverse weather conditions, deal with various traffic situations, and respond to emergencies within a controlled, risk-free environment. AR/VR technologies not only enhance practical skills but also help build confidence in decision-making under challenging conditions, ensuring that trainees are well-prepared for real-world maritime operations. Moreover, AR/VR can simulate emergency scenarios, such as fires, man-overboard incidents, or equipment failures, providing trainees with the opportunity to practice safety drills and emergency responses. These simulations enable students to experience high-pressure situations in a safe and controlled environment, helping them develop critical decision-making skills, improve their reaction times, and reinforce their ability to respond effectively to emergencies on board, ultimately enhancing their preparedness for real-life situations. AR/VR glasses can display step-by-step instructions or highlight specific parts of the machinery that need attention. This immersive technology enables trainees to interact directly with equipment, improving their hands-on skills and understanding of complex systems. By providing real-time guidance, AR helps ensure accuracy and efficiency in maintenance tasks, while also enhancing safety by reducing the risk of errors during repairs and inspections. Similarly, AR/VR applications can teach students to deal with bridge management issues as well as receive experts’ advice remotely.

Autonomous surface vessels can be incorporated into training simulators, enabling students to interact with and control these ships within a virtual environment. Students can gain hands-on experience in programming, monitoring, and intervening in the operations of autonomous ships, all while navigating through diverse conditions such as busy ports, harsh weather conditions, or responding to system malfunctions. This allows students to develop critical skills in managing autonomous technology, ensuring that they are prepared to handle complex scenarios and maintain operational efficiency in real-world maritime operations. Moreover, autonomous surface ships offer maritime students a unique platform to engage in research and development projects. Students can contribute to enhancing algorithms, testing new sensor technologies, or creating innovative applications for autonomous systems. These projects can be carried out in collaboration with industry partners, providing students with valuable, hands-on experience that bridges the gap between academic learning and real-world industry needs. Such initiatives not only foster innovation but also prepare students for future careers in the rapidly evolving maritime sector.

AI and big data analytics can be utilized to assist maritime students in developing their skills on how to forecast equipment failures and optimize maintenance plans. By analyzing data from sensors on engines, hulls, and other key systems, students can identify patterns, through predictive analytics, and anticipate when maintenance is required, helping to minimize the risk of unforeseen breakdowns. Moreover, students can utilize AI-powered tools that analyze large datasets, such as weather patterns, ocean currents, and historical voyage data, to optimize shipping routes. This training enables them to understand how to reduce fuel consumption, minimize emissions, and enhance safety by choosing the most efficient and secure routes. Since environmental protection is a high priority for the maritime industry, students may take advantage of applying AI and machine learning methods in order to monitor environmental factors like emissions, ballast water discharge, and noise pollution from ships. Students can learn to use these technologies to ensure adherence to international regulations. AI also enables students to practice in cases pertaining to crew and stress management.

With the proliferation of Internet-based applications, cybersecurity is a major concern. Cybersecurity and secure communication exercises can help students learn how to identify vulnerabilities, implement protective measures, and respond effectively to cybersecurity incidents on maritime vessels and infrastructure. Students can deepen their understanding of international regulations, such as the International Maritime Organization’s (IMO) guidelines on maritime cyber risk management, and apply these to develop strong safety frameworks for vessels and maritime organizations.

Simulators, although already in use in many universities and merchant marine academies, represent promising opportunities by exploiting new technological capabilities. High-fidelity bridge simulators can replicate the experience of navigating a vessel under various conditions, such as changing weather, sea states, and traffic scenarios. Students can practice essential skills like ship handling, collision avoidance, and emergency maneuvers in a safe, controlled environment that closely mirrors real-world situations. This immersive training, especially if combined with AR/VR, allows students to develop critical decision-making and operational skills, enhancing their ability to respond effectively to challenging maritime conditions while minimizing risks and ensuring safety on board. Simulators designed for port and vessel traffic management offer a realistic environment where students can practice coordinating ship movements in and out of ports, managing docking operations, and ensuring the safe passage of vessels through congested shipping lanes. These simulations can integrate real-time data and complex traffic scenarios, allowing trainees to experience and respond to dynamic conditions. By simulating high-pressure situations, students can develop critical skills in navigation, communication, and decision-making, preparing them for the complexities of managing vessel traffic in busy ports and maritime routes.

With respect to the limitations of this study, this study analyzes data that were selected from Greece. Data collected from other countries with strong maritime industries should provide more comprehensive insights regarding maritime education trends. The analysis of international datasets will enable cross-country comparisons, allowing for the identification of similarities and differences in the perceptions of educators, professionals, and students worldwide. Additionally, these comparisons will help highlight the expected impact of emerging technologies on maritime education for each country. Furthermore, the findings could be generalized to maritime education beyond Greece if data on country-level parameters, such as digital infrastructure and services in the maritime industry, as well as human capital (e.g., the digital skills of professors, professionals, and students), are collected on an international scale and incorporated into FAHP analysis. This would allow for an examination of how technological priorities and impacts vary, taking into account the digital maturity of the maritime industry in each country. Furthermore, assessing the effectiveness of the proposed approach to designing education policies for the adoption of new technologies would require longitudinal data collection and analysis, while also revealing new dimensions to consider in education policy-making.

Future research could focus on incorporating maritime students’ perspectives into the modeling process, allowing curriculum design and education policy-making to adopt a more comprehensive approach. This study analyzes data collected from maritime professors and maritime professionals in Greece. Expanding the scope of the study to include students’ and maritime professors and practitioners’ responses from an international perspective would shed light on how different organizational, technological, and cultural settings approach maritime education. Research could also aim to match students’ learning styles with the most appropriate ways of introducing new technologies into the learning process. Examining the impact of the selected technologies on educational Key Performance Indicators (KPIs) is also a topic that should be investigated, thus developing more elaborate maritime education policy-making models.

5. Conclusions

This study proposes the use of fuzzy logic to assess the significance of new technologies in maritime education and to design curricula that effectively incorporate these technologies. Fuzzy logic provides the means to deal with impartial information, associated with judging the potential of new technologies and their applications in industry or education. Furthermore, it provides the theoretical background necessary to adjust curricula content and structure based on the unique needs of different countries or educational institutions, as well as allowing for the application of AI from a human perspective by considering human conditions and contexts.